MCQOPTIONS
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MCQOPTIONS
This section includes 14 Mcqs, each offering curated multiple-choice questions to sharpen your Neural Networks knowledge and support exam preparation. Choose a topic below to get started.
| 1. |
Linear neurons can be useful for application such as interpolation, is it true? |
| A. | yes |
| B. | no |
| Answer» B. no | |
| 2. |
What is the objective of a pattern storage task in a network? |
| A. | to store a given set of patterns |
| B. | to recall a give set of patterns |
| C. | both to store and recall |
| D. | none of the mentioned |
| Answer» D. none of the mentioned | |
| 3. |
If input is ‘ a(l) + e ‘ where ‘e’ is the noise introduced, then what is the output if system is interpolative in nature? |
| A. | a(l) |
| B. | a(l) + e |
| C. | could be either a(l) or a(l) + e |
| D. | e |
| Answer» C. could be either a(l) or a(l) + e | |
| 4. |
If input is ‘ a(l) + e ‘ where ‘e’ is the noise introduced, then what is the output if system is accretive in nature? |
| A. | a(l) |
| B. | a(l) + e |
| C. | could be either a(l) or a(l) + e |
| D. | e |
| Answer» B. a(l) + e | |
| 5. |
If input is ‘ a(l) + e ‘ where ‘e’ is the noise introduced, then what is the output in case of autoassociative feedback network? |
| A. | a(l) |
| B. | a(l) + e |
| C. | could be either a(l) or a(l) + e |
| D. | e |
| Answer» C. could be either a(l) or a(l) + e | |
| 6. |
WHAT_IS_THE_OBJECTIVE_OF_A_PATTERN_STORAGE_TASK_IN_A_NETWORK??$ |
| A. | to store a given set of patterns |
| B. | to recall a give set of patterns |
| C. | both to store and recall |
| D. | none of the mentioned |
| Answer» D. none of the mentioned | |
| 7. |
Linear_neurons_can_be_useful_for_application_such_as_interpolation,_is_it_true?$ |
| A. | yes |
| B. | no |
| Answer» B. no | |
| 8. |
What property should a feedback network have, to make it useful for storing information? |
| A. | accretive behaviour |
| B. | interpolative behaviour |
| C. | both accretive and interpolative behaviour |
| D. | none of the mentioned |
| Answer» B. interpolative behaviour | |
| 9. |
If input is ‘ a(l) + e ‘ where ‘e’ is the noise introduced, then what is the output if system is interpolative in nature?# |
| A. | a(l) |
| B. | a(l) + e |
| C. | could be either a(l) or a(l) + e |
| D. | e |
| Answer» C. could be either a(l) or a(l) + e | |
| 10. |
If input is ‘ a(l) + e ‘ where ‘e’ is the noise introduced, then what is the output in case of autoassociative feedback network?$ |
| A. | a(l) |
| B. | a(l) + e |
| C. | could be either a(l) or a(l) + e |
| D. | e |
| Answer» C. could be either a(l) or a(l) + e | |
| 11. |
Is there any error in linear autoassociative networks? |
| A. | yes |
| B. | no |
| Answer» C. | |
| 12. |
What is objective of linear autoassociative feedforward networks? |
| A. | to associate a given pattern with itself |
| B. | to associate a given pattern with others |
| C. | to associate output with input |
| D. | none of the mentioned |
| Answer» B. to associate a given pattern with others | |
| 13. |
What is a Boltzman machine? |
| A. | A feedback network with hidden units |
| B. | A feedback network with hidden units and probabilistic update |
| C. | A feed forward network with hidden units |
| D. | A feed forward network with hidden units and probabilistic update |
| Answer» C. A feed forward network with hidden units | |
| 14. |
How can false minima be reduced in case of error in recall in feedback neural networks? |
| A. | by providing additional units |
| B. | by using probabilistic update |
| C. | can be either probabilistic update or using additional units |
| D. | none of the mentioned |
| Answer» C. can be either probabilistic update or using additional units | |